# Deep Learning Based Cloud Cover Parameterization for ICON

arXiv: https://arxiv.org/abs/2112.11317 · HTML: https://mlsanity.com/paper/2112.11317/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 87 references that are in the map.
- First posted on arXiv 21 Dec 2021.

## Builds on (most cited first, 10 of 10)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Learning Important Features Through Propagating Activation Differences](https://mlsanity.com/paper/1704.02685/) (2017)
- [Deep learning to represent sub-grid processes in climate models](https://mlsanity.com/paper/1806.04731/) (2018)
- [Machine Learning for Stochastic Parameterization: Generative Adversarial Networks in the Lorenz '96 Model](https://mlsanity.com/paper/1909.04711/) (2019)
- [Climate-Invariant Machine Learning](https://mlsanity.com/paper/2112.08440/) (2021)
- [A Fortran-Keras Deep Learning Bridge for Scientific Computing](https://mlsanity.com/paper/2004.10652/) (2020)
- [Sherpa: Robust Hyperparameter Optimization for Machine Learning](https://mlsanity.com/paper/2005.04048/) (2020)
- [Towards Physically-consistent, Data-driven Models of Convection](https://mlsanity.com/paper/2002.08525/) (2020)
- [Generative Modeling for Atmospheric Convection](https://mlsanity.com/paper/2007.01444/) (2020)

## Built on by (most cited first, 5 of 5)

- [Towards Physically Consistent Deep Learning For Climate Model Parameterizations](https://mlsanity.com/paper/2406.03920/) (2024)
- [mloz: A Highly Efficient Machine Learning-Based Ozone Parameterization for Climate Sensitivity Simulations](https://mlsanity.com/paper/2509.20422/) (2025)
- [Comparing Data-Driven and Mechanistic Models for Predicting Phenology in Deciduous Broadleaf Forests](https://mlsanity.com/paper/2401.03960/) (2024)
- [Multidata Causal Discovery for Statistical Hurricane Intensity Forecasting](https://mlsanity.com/paper/2510.02050/) (2025)
- [Decomposing Ensemble Spread in Lorenz '96 With Learned Stochastic Parameterizations](https://mlsanity.com/paper/2605.22242/) (2026)

## Abstract (arXiv)

A promising approach to improve cloud parameterizations within climate models and thus climate projections is to use deep learning in combination with training data from storm-resolving model (SRM) simulations. The ICOsahedral Non-hydrostatic (ICON) modeling framework permits simulations ranging from numerical weather prediction to climate projections, making it an ideal target to develop neural network (NN) based parameterizations for sub-grid scale processes. Within the ICON framework, we train NN based cloud cover parameterizations with coarse-grained data based on realistic regional and global ICON SRM simulations. We set up three different types of NNs that differ in the degree of vertical locality they assume for diagnosing cloud cover from coarse-grained atmospheric state variables. The NNs accurately estimate sub-grid scale cloud cover from coarse-grained data that has similar geographical characteristics as their training data. Additionally, globally trained NNs can reproduce sub-grid scale cloud cover of the regional SRM simulation. Using the game-theory based interpretability library SHapley Additive exPlanations, we identify an overemphasis on specific humidity and cloud ice as the reason why our column-based NN cannot perfectly generalize from the global to the regional coarse-grained SRM data. The interpretability tool also helps visualize similarities and differences in feature importance between regionally and globally trained column-based NNs, and reveals a local relationship between their cloud cover predictions and the thermodynamic environment. Our results show the potential of deep learning to derive accurate yet interpretable cloud cover parameterizations from global SRMs, and suggest that neighborhood-based models may be a good compromise between accuracy and generalizability.